MATLAB: Training multilabel data with traingdm function of Neural network toolbox

Deep Learning Toolboxmultilabelneural networktraingdm

Hello Guyz
i was wondering is there any way to train multilabel-data (i.e more than 1 class labels in output for single instance ) using traingdm (gradient decent with momentum)?
here, every col of p corresponds to one output value of t
p = [-1 -1 2 2;0 5 0 5];
t = [-1 -1 1 1];
net=newff(minmax(p),[3,1],{'tansig','purelin'},'traingdm')
but i need something like this
p = [-1 -1 2 2;0 5 0 5]; t = [-1 -1 1 1 ; 1 1 -1 1 ; 1 1 1 -1 ; -1 1 -1 1];
i.e each column (instance) of p corresponds to set of output values (in this case 4)
is this possible ?
Please Help …Thank you so much !!

Best Answer

For classification/pattern-recognition, newpr is favored over newff
For regression/curve-fitting, newfit is favored over newff
Both call newff but all three have different default plot selections
However, all three are doubly obsolete. So, if you have them, use the current functions patternnet or fitnet which call feedforwardnet.
When designing a classifier with c mutually exclusive classes, target should contain columns of the c-dimensional unit matrix eye(c). The row index of the single 1 is the corresponding class index in [1,c]. The relationships are
 target     = ind2vec(trueclass)
 trueclass  = vec2ind(target)
 [ net tr output RegressErr] = train(...)% RegressErr = target-output
 assignedclass = vec2ind(output)
 ClassErr      = (assignedclass ~= trueclass);
 Nerr          = sum(ClassErr)
 PctErr        = 100*Nerr/N
% For trn/val/tst breakdowns of the results, use tr.trainInd, etc
Hope this helps.
Thank you for formally accepting my answer
Greg
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